CiteULike: bigga's real-time

Characterizing the variability of arrival processes with indexes of dispersion

R Gusella — 2008-07-04T13:03:47-00:00

Selected Areas in Communications, IEEE Journal on, Vol. 9, No. 2. (1991), pp. 203-211.

The author proposes to characterize the burstiness of packet-arrival processes with indexes of dispersion for intervals and counts. These indexes, which are functions of the variance of intervals and counts, are relatively straightforward and can estimate and convey much more information than simpler indexes (such as the coefficient of variation) that are often used to describe burstiness quantitatively. The author defines and evaluates the indexes of dispersion for some of the simple analytical models that are frequently used to represent highly variable processes. The indexes for a number of measured point processes that were generated by workstations communicating to file servers over a local area network are also estimated. It is shown that nonstationary components in the measured packet-arrival data distort the shape of the indexes, and ways to handle nonstationary data are proposed. To show how to incorporate measures of variability into analytical models and to offer an example of how to model the measured packet-arrival processes, the author describes a fitting procedure based on the index of dispersion for counts for the Markov-modulated Poisson process

Learning for Accurate Classification of Real-time Traffic

Wei — 2008-02-29T09:52:43-00:00

Offline/realtime traffic classification using semi-supervised learning

Jeffrey Erman — 2008-02-15T14:50:30-00:00

Performance Evaluation, Vol. 64, No. 9-12. (October 2007), pp. 1194-1213.

Identifying and categorizing network traffic by application type is challenging because of the continued evolution of applications, especially of those with a desire to be undetectable. The diminished effectiveness of port-based identification and the overheads of deep packet inspection approaches motivate us to classify traffic by exploiting distinctive flow characteristics of applications when they communicate on a network. In this paper, we explore this latter approach and propose a semi-supervised classification method that can accommodate both known and unknown applications. To the best of our knowledge, this is the first work to use semi-supervised learning techniques for the traffic classification problem. Our approach allows classifiers to be designed from training data that consists of only a few labeled and many unlabeled flows. We consider pragmatic classification issues such as longevity of classifiers and the need for retraining of classifiers. Our performance evaluation using empirical Internet traffic traces that span a 6-month period shows that: (1) high flow and byte classification accuracy (i.e., greater than 90%) can be achieved using training data that consists of a small number of labeled and a large number of unlabeled flows; (2) presence of "mice" and "elephant" flows in the Internet complicates the design of classifiers, especially of those with high byte accuracy, and necessitates the use of weighted sampling techniques to obtain training flows; and (3) retraining of classifiers is necessary only when there are non-transient changes in the network usage characteristics. As a proof of concept, we implement prototype offline and realtime classification systems to demonstrate the feasibility of our approach.

Fast, Accurate, and Lightweight Real-Time Traffic Identification Method Based on Flow Statistics

Masaki Tai — 2008-01-31T04:53:03-00:00

Passive and Active Network Measurement (2007), pp. 255-259.

Recently, identification of real-time traffic online is a key technology to achieve different service to real-time and bulk applications. Previously it is easy to identify real-time by checking the protocol/port number in IP header, however, it becomes more difficult due to the existence of streaming traffic over TCP connection, overlay networks such as P2P and VPN. In this paper, we propose a new identification method for real-time traffic based on not checking the protocol number, but analyzing the statistical characteristics of packet arrivals. Our approach is fast, accurate and lightweight compared to conventional techniques.

Automated network games enhancement layer: a proposed architecture

Jason But — 2007-07-27T13:41:17-00:00

(2006)

In this paper we present the design of the Automated Network Games Enhancement Layer (ANGEL), a novel architecture for meeting Quality of Service (QoS) requirements of real-time network game traffic across consumer broadband links. Consumer access links can become bottlenecks when faced with heterogeneous network traffic (e.g. simultaneous use of online games and peer-to-peer file sharing) and the online gaming experience can be significantly affected by bottleneck queuing. Implementing QoS on these links provides improvement by reducing latency and jitter. In our approach network servers automatically identify traffic that might benefit from QoS and then trigger provisioning of QoS by signaling network elements such as access routers. By placing intelligence within the network, QoS decisions can be transparently made for the game applications without imposing an additional processing cost at the access link router. Our system uniquely uses machine learning methods to perform traffic classification.

Streaming video over the Internet: approaches and directions

Dapeng Wu — 2006-07-14T03:14:04-00:00

Circuits and Systems for Video Technology, IEEE Transactions on, Vol. 11, No. 3. (2001), pp. 282-300.

Due to the explosive growth of the Internet and increasing demand for multimedia information on the Web, streaming video over the Internet has received tremendous attention from academia and industry. Transmission of real-time video typically has bandwidth, delay, and loss requirements. However, the current best-effort Internet does not offer any quality of service (QoS) guarantees to streaming video. Furthermore, for video multicast, it is difficult to achieve both efficiency and flexibility. Thus, Internet streaming video poses many challenges. In this article we cover six key areas of streaming video. Specifically, we cover video compression, application-layer QoS control, continuous media distribution services, streaming servers, media synchronization mechanisms, and protocols for streaming media. For each area, we address the particular issues and review major approaches and mechanisms. We also discuss the tradeoffs of the approaches and point out future research directions

A survey of packet loss recovery techniques for streaming audio

C Perkins — 2007-05-11T15:22:33-00:00

Network, IEEE, Vol. 12, No. 5. (1998), pp. 40-48.

We survey a number of packet loss recovery techniques for streaming audio applications operating using IP multicast. We begin with a discussion of the loss and delay characteristics of an IP multicast channel, and from this show the need for packet loss recovery. Recovery techniques may be divided into two classes: sender- and receiver-based. We compare and contrast several sender-based recovery schemes: forward error correction (both media-specific and media-independent), interleaving, and retransmission. In addition, a number of error concealment schemes are discussed. We conclude with a series of recommendations for repair schemes to be used based on application requirements and network conditions

RTP: A Transport Protocol for Real-Time Applications

Schulzrinne — 2006-06-14T22:21:49-00:00

Internet-Draft ietf-avt-rtp-new-01.txt (work inprogress) (1998)

This memorandum describes the real-time transport protocol, RTP. RTP provides end-toend network transport functions suitable for applications transmitting real-time data, such as audio, video or simulation data over multicast or unicast network services. RTP does not address resource reservation and does not guarantee quality-of-service for real-time services. The data transport is augmented by a control protocol (RTCP) designed to provide minimal control and identification functionality...

On the Reliability of AI Planning Software in Real-Time Applications

Ing-Ray Chen — 2007-04-03T07:24:25-00:00

IEEE Transactions on Knowledge and Data Engineering, Vol. 7, No. 1. (February 1995), pp. 4-13.

Depth-limited search for real-time problem solving

Richard Korf — 2007-04-03T07:14:30-00:00

Real-Time Systems, Vol. 2, No. 1. (1 May 1990), pp. 7-24.